Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory332.0 KiB
Average record size in memory34.0 B

Variable types

Categorical1
Numeric2

Dataset

Descriptionㅇ ‘20.12.21. 안심코드앱 출시 이후 안심코드를 설치한 매장(비식별) 및 업종별 월 방문객 추이 데이터 ㅇ 데이터 기간 : '20.12.26.~'22. 2.18. ㅇ 데이터 분류 - shop-type : 업종/ shop-id : 제주안심코드를 신청한 사업장 순번대로 부여된 id/ visit_count : 방문자 수 ㅇ 각 파일은 ‘20.12.26.(토)부터 28일(4주)째 금요일까지의 데이터임.(각 파일별 연속) * 토요일~28일째 금요일
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/1284

Alerts

visit_count is highly skewed (γ1 = 35.37431842)Skewed
shop_id has unique valuesUnique

Reproduction

Analysis started2023-12-11 20:00:33.951409
Analysis finished2023-12-11 20:00:34.721556
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

shop_type
Categorical

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3817 
일반음식점
3091 
휴게음식점
692 
미용업
 
376
실내 체육시설
 
263
Other values (30)
1761 

Length

Max length10
Median length8
Mean length3.8804
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row병‧의원
2nd row기타
3rd row일반음식점
4th row일반음식점
5th row체인화 편의점

Common Values

ValueCountFrequency (%)
기타 3817
38.2%
일반음식점 3091
30.9%
휴게음식점 692
 
6.9%
미용업 376
 
3.8%
실내 체육시설 263
 
2.6%
공공기관 231
 
2.3%
병‧의원 215
 
2.1%
숙박업(일반,생활) 170
 
1.7%
종교시설 154
 
1.5%
체인화 편의점 130
 
1.3%
Other values (25) 861
 
8.6%

Length

2023-12-12T05:00:34.803567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 3817
36.7%
일반음식점 3091
29.7%
휴게음식점 692
 
6.7%
미용업 376
 
3.6%
실내 263
 
2.5%
체육시설 263
 
2.5%
공공기관 231
 
2.2%
병‧의원 215
 
2.1%
숙박업(일반,생활 170
 
1.6%
종교시설 154
 
1.5%
Other values (27) 1121
 
10.8%

shop_id
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35360.321
Minimum625
Maximum71132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:34.938505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum625
5-th percentile3215.75
Q118368.75
median33999
Q351971.25
95-th percentile68386.45
Maximum71132
Range70507
Interquartile range (IQR)33602.5

Descriptive statistics

Standard deviation20863.839
Coefficient of variation (CV)0.59003534
Kurtosis-1.1870953
Mean35360.321
Median Absolute Deviation (MAD)16821
Skewness0.075153935
Sum3.5360321 × 108
Variance4.3529979 × 108
MonotonicityNot monotonic
2023-12-12T05:00:35.074495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9174 1
 
< 0.1%
29367 1
 
< 0.1%
12835 1
 
< 0.1%
69258 1
 
< 0.1%
63057 1
 
< 0.1%
61053 1
 
< 0.1%
25420 1
 
< 0.1%
53907 1
 
< 0.1%
37981 1
 
< 0.1%
61698 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
625 1
< 0.1%
627 1
< 0.1%
633 1
< 0.1%
639 1
< 0.1%
640 1
< 0.1%
650 1
< 0.1%
651 1
< 0.1%
655 1
< 0.1%
663 1
< 0.1%
668 1
< 0.1%
ValueCountFrequency (%)
71132 1
< 0.1%
71110 1
< 0.1%
71099 1
< 0.1%
71060 1
< 0.1%
70997 1
< 0.1%
70994 1
< 0.1%
70977 1
< 0.1%
70962 1
< 0.1%
70952 1
< 0.1%
70950 1
< 0.1%

visit_count
Real number (ℝ)

SKEWED 

Distinct1344
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.7496
Minimum1
Maximum82538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:35.244304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median42
Q3204
95-th percentile1047
Maximum82538
Range82537
Interquartile range (IQR)194

Descriptive statistics

Standard deviation1621.2559
Coefficient of variation (CV)5.754244
Kurtosis1580.2207
Mean281.7496
Median Absolute Deviation (MAD)39
Skewness35.374318
Sum2817496
Variance2628470.8
MonotonicityNot monotonic
2023-12-12T05:00:35.408135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 537
 
5.4%
2 376
 
3.8%
3 316
 
3.2%
4 253
 
2.5%
5 247
 
2.5%
6 203
 
2.0%
8 189
 
1.9%
7 184
 
1.8%
10 169
 
1.7%
9 135
 
1.4%
Other values (1334) 7391
73.9%
ValueCountFrequency (%)
1 537
5.4%
2 376
3.8%
3 316
3.2%
4 253
2.5%
5 247
2.5%
6 203
 
2.0%
7 184
 
1.8%
8 189
 
1.9%
9 135
 
1.4%
10 169
 
1.7%
ValueCountFrequency (%)
82538 1
< 0.1%
81154 1
< 0.1%
61676 1
< 0.1%
42888 1
< 0.1%
40599 1
< 0.1%
18449 1
< 0.1%
18407 1
< 0.1%
17610 1
< 0.1%
15251 1
< 0.1%
14897 1
< 0.1%

Interactions

2023-12-12T05:00:34.372084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:34.149173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:34.467106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:34.258718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:00:35.504982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_typeshop_idvisit_count
shop_type1.0000.5160.496
shop_id0.5161.0000.042
visit_count0.4960.0421.000
2023-12-12T05:00:35.586542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_idvisit_countshop_type
shop_id1.000-0.0740.203
visit_count-0.0741.0000.210
shop_type0.2030.2101.000

Missing values

2023-12-12T05:00:34.574694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:00:34.670847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

shop_typeshop_idvisit_count
1082병‧의원91741615
9498기타6976211
23566일반음식점6029922
9526일반음식점45932210
14670체인화 편의점6960089
1835일반음식점505321111
20945기타1894133
23896기타4814721
29591기타213118
12166기타70938135
shop_typeshop_idvisit_count
16753체인화 편의점6398963
30565기타222597
33749기타527433
441실내 체육시설184392908
14953휴게음식점4695684
3038PC방8897751
22089숙박업(일반,생활)3257728
8339일반음식점68739256
1923일반음식점702881072
9600일반음식점41060207